Well, for me, consulting and co-founding companies. I co-founded Sight Commerce, a B2B machine learning technology company that applies makeup on the visages of models in milliseconds. Basically, my team and I noticed that makeup sessions for photo-shoots took a ton of time and were expensive. We thought, “why not reduce the cost by an order of magnitude with computer vision and machine learning tech?” Then, we created the tech and sold it to companies like Estee Lauder and Bloomingdales.

How does Sight Commerce work?

Sight Commerce takes a neutral photo,renders makeup (leveraging make-up data that has been fed to the vision AI algorithm), the software adds shine, shimmer, and all other qualities of the makeup, and you end up with photos that are wearing different kinds of makeup instantly.

Wow. So, the makeup we see on models in ads may actually be digitally placed on visages of models?

Exactly.

Moving on. What should we know about vision AIs today?

Well, the most important fact to be aware of is that vision AI accuracy is in the 95% range in certain applications. That’s all largely due to a new class of machine learning algorithms called deep learning. Due to these algorithmic breakthroughs, the amount of data available, computational power, and the GPU cloud (e.g. Amazon Web Services GPU) machine vision today is about as good as human vision in certain areas

And what’s to be said about old vision technology versus today?

Early vision technology worked off of hand-crafted features that fed into learning algorithms. Designing these features was a black art that required a lot of trial and error, and the same features did not always work on new problems. Now, deep learning automates feature design for engineers. It works by creating a bank of features that are learned on the fly based on the data provided - as opposed to static features designed manually by an engineer. As a consequence, Deep Learning has improved the accuracy of image recognition to a level that rivals humans

Does vision AI have the ability to learn completely on its own?

Not yet. Currently, most of AI learning is supervised learning. That means, engineers still feed the AI algorithm examples of images. For example, I will give an algorithm a series of cat images and say, “this is what a cat looks like.” From there, the algorithm learns the features required for detecting a cat in an image.

What is face processing and why is it important in the AI field?

Face processing is simply the analysis of facial images. A few reasons why face processing is important are: (1) Photo enhancement: people want to look good in their photos; and (2) Authentication: face recognition can be used as a form of identification (3) Face analysis for all sorts of applications like drowsy driver detection, video game control, emotion recognition, simulating cosmetic surgery to name a few.

Who will be successful in the AI B2B enterprise space?

Well AI to the B2B Enterprise space is what shovels were to the gold rush miners. It was the shovel business that made the money in the gold rush. In a similar manner, B2B enterprises who create AI tools will make the money.

I predict that the companies who create AI technologies that can apply across industries will come out on top.

Today, who is leading in AI: corporations or startups, and why?

It’s difficult for startups to lead because of the lack of data and resources. Startups may have access to 1 million images, for example, whereas a big corporation will have access to 5 billion images. Considering data is a key to AI, this is a big deal.

In terms of leaders: I would put my money on Facebook and Google who have a ton of data to work with. Baidu, Microsoft, and IBM are very strong as well. They have access to huge amounts of data, incredible resources, manpower, and research and development teams.

What piece of advice would you share with startups and corporations?

Use deep learning if you are currently using traditional computer vision techniques. Usually, companies have a vision system that works okay but deep learning could take them to a whole different level. Second, as Sundar Pichai of Google said: it is time for many tech companies to think ‘AI first’ instead of ‘Mobile first’.